Eryun Liu

Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine Ridge Structure Dictionary.

Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate (“lights-out” capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving “lights-out” latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher.

An effective biometric cryptosystem combining fingerprints with error correction codes

With the emergence and popularity of identity verification means by biometrics, the biometric system which can assure security and privacy has received more and more concentration from both the research and industry communities. In the field of secure biometric authentication, one branch is to combine the biometrics and cryptography. Among all the solutions in this branch, fuzzy commitment scheme is a pioneer and effective security primitive. In this paper, we propose a novel binary length-fixed feature generation method of fingerprint. The alignment procedure, which is thought as a difficult task in the encrypted domain, is avoided in the proposed method due to the employment of minutiae triplets. Using the generated binary feature as input and based on fuzzy commitment scheme, we construct the biometric cryptosystems by combining various of error correction codes, including BCH code, a concatenated code of BCH code and Reed-Solomon code, and LDPC code. Experiments conducted on three fingerprint databases, including one in-house and two public domain, demonstrate that the proposed binary feature generation method is effective and promising, and the biometric cryptosystem constructed by the feature outperforms most of the existing biometric cryptosystems in terms of ZeroFAR and security strength. For instance, in the whole FVC2002 DB2, a 4.58% ZeroFAR is achieved by the proposed biometric cryptosystem with the security strength 48 bits.

A Coarse to Fine Minutiae-Based Latent Palmprint Matching

With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm.

LFIQ: Latent fingerprint image quality

Latent fingerprint images are typically obtained under non-ideal acquisition conditions, resulting in incomplete or distorted impression of a finger, and ridge structure corrupted by background noise. This necessitates involving latent experts in latent fingerprint examination, including assessing the value of a latent print as forensic evidence. However, it is now generally agreed that human factors (e.g., human visual perception, expertise of latent examiners, workload, etc.) can significantly affect the reliability and consistency of the value determinations made by latent examiners. We propose an objective quality measure for latent fingerprints, called Latent Fingerprint Image Quality (LFIQ), that can be effectively used to distinguish latent fingerprints of good quality, which do not require any human intervention, and to compensate for the subjective nature of value determination by latent examiners. We investigate several factors that determine the latent quality: (i) ridge quality based on ridge clarity and connectivity of good ridge structures, (ii) minutiae reliability based on a minutiae dictionary learnt from high quality minutia patches, and (iii) position of the finger by detecting a reference point. The proposed LFIQ metric is based on triangulation of minutiae incorporating the above three factors. Experimental results show that (i) the proposed LFIQ is a good predictor of the latent matching performance by AFIS and (ii) it is also correlated with value determination by latent examiners.

Latent fingerprint indexing: Fusion of level 1 and level 2 features

Fingerprints have been widely used as a biometric trait for person recognition. Due to the wide acceptance and deployment of fingerprint matching systems, there is a steady increase in the size of fingerprint databases in law enforcement and national ID agencies. Thus, it is of great interest to develop methods that, for a given query fingerprint (rolled or latent), can efficiently filter out a large portion of the reference or background database based on a coarse matching (or indexing) strategy. In this work, we propose an indexing technique, primarily for latents, that combines multiple level 1 and level 2 features to filter out a large portion of the background database while maintaining the latent matching accuracy. Our approach consists of combining minutiae, singular points, orientation field and frequency information. Experimental results carried out on 258 latents in NIST SD27 against a large background database (267K rolled prints) show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. At a penetration rate of 20%, our approach can reach a hit rate of 90.3%, with a five-fold reduction in the latent search (indexing + matching) time, while maintaining the latent matching accuracy.

Fingerprint matching by incorporating minutiae discriminability

Traditional minutiae matching algorithms assume that each minutia has the same discriminability. However, this assumption is challenged by at least two facts. One of them is that fingerprint minutiae tend to form clusters, and minutiae points that are spatially close tend to have similar directions with each other. When two different fingerprints have similar clusters, there may be many well matched minutiae. The other one is that false minutiae may be extracted due to low quality fingerprint images, which result in both high false acceptance rate and high false rejection rate. In this paper, we analyze the minutiae discriminability from the viewpoint of global spatial distribution and local quality. Firstly, we propose an effective approach to detect such cluster minutiae which of low discriminability, and reduce corresponding minutiae similarity. Secondly, we use minutiae and their neighbors to estimate minutia quality and incorporate it into minutiae similarity calculation. Experimental results over FVC2004 and FVC-onGoing demonstrate that the proposed approaches are effective to improve matching performance.

Minutiae and modified Biocode fusion for fingerprint-based key generation

Key generation from biometrics has been studied intensively in recent years, linking a key with certain biometric enhances the strength of identity authentication. But the state-of-the-art key generation systems are far away from practicality due to low accuracy. The special manner of biometric matching makes a single feature based key generation system difficult to obtain a high recognition accuracy. Integrating more features into key generation system may be a potential solution to improve the system performance. In this paper, we propose a fingerprint based key generation system under the framework of fuzzy extractor by fusing two kinds of features: minutia-based features and image-based features. Three types of sketch, including minutiae based sketch, modified Biocode based sketch, and combined feature based sketch, are constructed to deal with the feature differences. Our system is tested on FVC2002 DB1 and DB2, and the experimental results show that the fusion scheme effectively improves the system performance compared with the systems based only on minutiae or modified Biocode.

A key binding system based on n-nearest minutiae structure of fingerprint

Biometric cryptosystem has gained increasing attention in recent years. One of the difficulties in this field is how to perform biometric matching under template protection. In this paper, we propose a key binding system based on n-nearest minutiae structures of fingerprint. Unlike the traditional fingerprint recognition method, the matching of nearest structures are totally performed in the encrypted domain, where the template minutiae are protected. Three levels of secure sketch are applied to deal with error correction and key binding: (1) The wrap-around construction is used to tolerate random errors that happens on paired minutiae; (2) the PinSketch construction is used to recover nearest structures which are disturbed by burst errors; and (3) Shamirs secret sharing scheme is used to bind and recover a key based on template minutia structures. The experimental results on FVC2002 DB1 and DB2 and security analysis show that our system is efficient and secure.

On latent fingerprint image quality

Latent fingerprints which are lifted from surfaces of objects at crime scenes play a very important role in identifying suspects in the crime scene investigations. Due to poor quality of latent fingerprints, automatic processing of latents can be extremely challenging. For this reason, latent examiners need to be involved in latent identification. To expedite the latent identification and alleviate subjectivity and inconsistency in latent examiners’ feature markups and decisions, there is a need to develop latent fingerprint identification systems that can operate in the “lights-out” mode. One of the most important steps in “lights-out” systems is to determine the quality of a given latent to predict the probability that the latent can be identified in a fully automatic manner. In this paper, we (i) propose a definition of latent value determination as a way of establishing the quality of latents based on a specific matcher’s identification performance, (ii) define a set of features based on ridge clarity and minutiae and evaluate them based on their capability to determine if a latent is of value for individualization or not, and (iii) propose a latent fingerprint image quality (LFIQ) that can be useful to reject the latents which cannot be successfully identified in the “lights-out” mode. Experimental results show that the most salient latent features include the average ridge clarity and the number of minutiae. The proposed latent quality measure improves the rank-100 identification rate from 69 % to 86 % by rejecting 50 % of latents deemed as poor quality. In addition, the rank-100 identification is 80 % when rejecting 80 % of the latents in the databases assessed as ‘NFIQ = 5’; however, the same identification rate can be achieved by rejecting only 21 % of the latents with low LFIQ.

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top K candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.